Existing works adopt effective multi-view capture for two reasons.
First, it makes monocular DVS more tractable.
Second, it enables evaluating novel view on the full image, without worrying about the visibility of each test pixel, as all camera views were visible during training. We show this effect in Figure REF .
When trained with camera teleportation (3rd column), the model can generate a high-quality full image from the test view.
However, when trained without camera teleportation (4th column), the model struggles to hallucinate unseen pixels since NeRFs [1]}, [2]} are not designed to predict completely unseen portions of the scene, unless they are specifically trained for generalization [3]}.
Next, we propose new metrics that enable evaluation without using camera teleportation. Note that when the model is trained without camera teleportation, the rendering quality also degrades, which we also evaluate.
